Gesture Recognition Framework Research Articles

Hybrid Convolution-Based Efficientnet-Based Hand Gesture Recognition Framework with Optimized Algorithm

The difficulties in communication and hearing are an important concern for deaf–dumb people, which stop access to their essential and basic needs. Many findings have been made to address sign languages even though this challenging problem is not still solved. Many methods aimed to propose vision-based classifiers through identical pattern investigation tasks by obtaining the difficult handcraft feature descriptions of gestures from the gathered images. However, the efficacy of all those models is less for performing with a huge signbook captured from uncontrolled and complex background conditions. So, an effective Indian Sign Language (ISL) classification method is developed by an advanced deep learning approach. At first, the hand gesture images are obtained from the data source. Only the image of the hand, even from a complicated background, is extracted from the obtained image. The features are extracted using the Scale-Invariant Feature Transform (SIFT) method and Multiscale Vision Transformer (MVT). Then, the extracted features are fed to the Hybrid Convolution-based EfficientNet (HCEN) model. The hyper-parameters in the developed HCEN model are tuned using the implemented Adaptive Political Optimizer (APO) algorithm. The recognized hand signs are obtained from the suggested HCEN model. Various experiments are conducted to determine the performance of the suggested deep learning-based hand gesture recognition model.

Active convolutional neural networks sign language (ActiveCNN-SL) framework: a paradigm shift in deaf-mute communication

Real-time speech-to-text and text-to-speech technologies have significantly influenced the accessibility of communication for individuals who are deaf or mute. This research aims to assess the efficacy of these technologies in facilitating communication between deaf or mute individuals and those who are neither deaf nor mute. A mixed-method approach will incorporate qualitative and quantitative data collection and analysis techniques. The study will involve participants from deaf or mute and non-deaf or non-mute communities. The research will scrutinize the precision and efficiency of communication using these technologies and evaluate user experience and satisfaction. Furthermore, the study intends to pinpoint potential obstacles and limitations of these technologies and offer suggestions for enhancing their effectiveness in fostering inclusivity. The study proposes an active learning framework for sign language gesture recognition, termed Active Convolutional Neural Networks—Sign Language (ActiveCNN-SL). ActiveCNN-SL aims to minimize the labeled data required for training and augment the accuracy of sign language gesture recognition through iterative human feedback. This proposed framework holds the potential to enhance communication accessibility for deaf and mute individuals and encourage inclusivity across various environments. The proposed framework is trained using two primary datasets: (i) the Sign Language Gesture Images Dataset and (ii) the American Sign Language Letters (ASL)—v1. The framework employs Resnet50 and YoloV.8 to train the datasets. It has demonstrated high performance in terms of precision and accuracy. The ResNet model achieved a remarkable accuracy rate of 99.98% during training, and it also exhibited a validation accuracy of 100%, surpassing the baseline CNN and RNN models. The YOLOv8 model outperformed previous methods on the ASL alphabet dataset, achieving an overall mean average accuracy for all classes of 97.8%.

Wi-AM: Enabling Cross-Domain Gesture Recognition with Commodity Wi-Fi.

RF-based gesture recognition systems outperform computer vision-based systems in terms of user privacy. The integration of Wi-Fi sensing and deep learning has opened new application areas for intelligent multimedia technology. Although promising, existing systems have multiple limitations: (1) they only work well in a fixed domain; (2) when working in a new domain, they require the recollection of a large amount of data. These limitations either lead to a subpar cross-domain performance or require a huge amount of human effort, impeding their widespread adoption in practical scenarios. We propose Wi-AM, a privacy-preserving gesture recognition framework, to address the above limitations. Wi-AM can accurately recognize gestures in a new domain with only one sample. To remove irrelevant disturbances induced by interfering domain factors, we design a multi-domain adversarial scheme to reduce the differences in data distribution between different domains and extract the maximum amount of transferable features related to gestures. Moreover, to quickly adapt to an unseen domain with only a few samples, Wi-AM adopts a meta-learning framework to fine-tune the trained model into a new domain with a one-sample-per-gesture manner while achieving an accurate cross-domain performance. Extensive experiments in a real-world dataset demonstrate that Wi-AM can recognize gestures in an unseen domain with average accuracy of 82.13% and 86.76% for 1 and 3 data samples.

Hand gesture recognition based on micro‐Doppler radar using graph neural network

AbstractHand gesture recognition based on micro‐Doppler (MD) radar has garnered considerable attention from researchers as a potential method for human–computer interaction (HCI). However, two significant challenges, that is, obvious differences in MD map size and lots of redundant information contained in the MD maps, are encountered. Here, a multi‐scale graph‐based hand gesture recognition framework is proposed. First, a MD graph representation method is developed, which is adapted to arbitrary‐sized frames and enables to map the key features in a sparse manner. Then, the multi‐scale information from MD graph is fully extracted for hand gesture recognition. Experimental results show that the proposed framework achieves a state‐of‐the‐art accuracy of 96.73% on the four‐class radar hand gesture dataset, while reducing up to 99% of the redundant information in the MD maps. This framework requires only a little amount of memory storage with good hand gesture recognition capability, demonstrating its high potential in the HCI field.

Integrated block-wise neural network with auto-learning search framework for finger gesture recognition using sEMG signals

Accurate finger gesture recognition with surface electromyography (sEMG) is essential and long-challenge in the muscle-computer interface, and many high-performance deep learning models have been developed to predict gestures. For these models, problem-specific tuning of network architecture is essential for improving the performance, yet it requires substantial knowledge of network architecture design and commitment of time and effort. This process thus imposes a major obstacle to the widespread and flexible application of modern deep learning. To address this issue, we present an auto-learning search framework (ALSF) to generate the integrated block-wised neural network (IBWNN) for sEMG-based gesture recognition. IBWNN contains several feature extraction blocks and dimensional reduction layers, and each feature extraction block integrates two sub-blocks (i.e., multi-branch convolutional block and triplet attention block). Meanwhile, ALSF generates optimal models for gesture recognition through the reinforcement learning method. The results show that the generated models yield state-of-the-art results compared to the modern popular networks on the open dataset Ninapro DB5. Moreover, compared to other networks, the generated models have fewer parameters and can be deployed in practical applications with less resource consumption.

A Robust and Real-Time Framework of Cross-Subject Myoelectric Control Model Calibration via Multi-Source Domain Adaptation.

Surface electromyogram (sEMG) has been widely used in hand gesture recognition. However, most previous studies focused on user-personalized models, which require a great amount of data from each new target user to learn the user-specific EMG patterns. In this work, we present a novel real-time gesture recognition framework based on multi-source domain adaptation, which learns extra knowledge from the data of other users, thereby reducing the data collection burdens on the target user. Additionally, compared with conventional domain adaptation methods which treat data from all users in the source domain as a whole, the proposed multi-source method treat data from different users as multiple separate source domains. Therefore, more detailed statistical information on the data distribution from each user can be learned effectively. High-density sEMG (256 channels) from 20 subjects was used to validate the proposed method. Importantly, we evaluated our method with a simulated real-time processing pipeline on continuous sEMG data stream, rather than well-segmented data. The false alarm rate during rest periods in an EMG data stream, which is typically neglected by previous studies performing offline analyses, was also considered. Our results showed that, with only 1 s sEMG data per gesture from the new user, the 10-gesture classification accuracy reached 87.66 % but the false alarm rate was reduced to 1.95 %. Our method can reduce the frustratingly heavy data collection burdens on each new user.

RimSense

Smart eyewear's interaction mode has attracted significant research attention. While most commercial devices have adopted touch panels situated on the temple front of eyeglasses for interaction, this paper identifies a drawback stemming from the unparalleled plane between the touch panel and the display, which disrupts the direct mapping between gestures and the manipulated objects on display. Therefore, this paper proposes RimSense, a proof-of-concept design for smart eyewear, to introduce an alternative realm for interaction - touch gestures on eyewear rim. RimSense leverages piezoelectric (PZT) transducers to convert the eyeglass rim into a touch-sensitive surface. When users touch the rim, the alteration in the eyeglass's structural signal manifests its effect into a channel frequency response (CFR). This allows RimSense to recognize the executed touch gestures based on the collected CFR patterns. Technically, we employ a buffered chirp as the probe signal to fulfil the sensing granularity and noise resistance requirements. Additionally, we present a deep learning-based gesture recognition framework tailored for fine-grained time sequence prediction and further integrated with a Finite-State Machine (FSM) algorithm for event-level prediction to suit the interaction experience for gestures of varying durations. We implement a functional eyewear prototype with two commercial PZT transducers. RimSense can recognize eight touch gestures on the eyeglass rim and estimate gesture durations simultaneously, allowing gestures of varying lengths to serve as distinct inputs. We evaluate the performance of RimSense on 30 subjects and show that it can sense eight gestures and an additional negative class with an F1-score of 0.95 and a relative duration estimation error of 11%. We further make the system work in real-time and conduct a user study on 14 subjects to assess the practicability of RimSense through interactions with two demo applications. The user study demonstrates RimSense's good performance, high usability, learnability and enjoyability. Additionally, we conduct interviews with the subjects, and their comments provide valuable insight for future eyewear design.

A surface electromyography based hand gesture recognition framework leveraging variational mode decomposition technique and deep learning classifier

Deciphering and classifying surface electromyography (sEMG) signals is highly essential in rehabilitation robotics, myoelectric prosthetic control, sign languages and human–computer-interface. Researchers have generally focused on utilizing multi-channel sEMG signals for gesture classification. However, in case of amputees, the residual muscles are limited and the gesture classification needs to be reframed with minimum sEMG channels. Moreover, another key problem in the gesture classification system is the nonlinear and non-stationary nature of sEMG signals, leading to poor generalization ability. Hence, to address the aforementioned problems, this study puts forward a novel deep learning classifier framework which leverages the potentials of variational mode decomposition (VMD) technique and a hybrid convolutional neural network–long short term memory (CNN–LSTM) classifier model to recognize the hand gestures from single channel sEMG signals. Collecting the sEMG signals from forearm muscles of 25 intact subjects for ten functional and grasping actions, this work implements a VMD technique to identify the prominent frequency modes in the sEMG signals. From the decomposed modes of sEMG signals, the prominent intrinsic mode functions (IMFs) are extracted through spectral analysis to minimize the computation burden on the hybrid classifier model. Furthermore, as the muscle contractions result in substantial temporal dependencies, this work exploits the potentials of CNN and LSTM networks and extracts the spatiotemporal features of the sEMG signals for various hand gestures. The experimental results corroborate that the proposed classifier framework can achieve an average classification accuracy of 98.04% and provide 3% improvement in the classification accuracy compared to conventional CNN classifier.

A Low-Complexity Hand Gesture Recognition Framework via Dual mmWave FMCW Radar System.

In this paper, we propose a novel low-complexity hand gesture recognition framework via a multiple Frequency Modulation Continuous Wave (FMCW) radar-based sensing system. In this considered system, two radars are deployed distributively to acquire motion vectors from different observation perspectives. We first independently extract reflection points of the interested target from different radars by applying the proposed neighboring reflection points detection method after processing the traditional 2-dimensional Fast Fourier Transform (2D-FFT). The obtained sufficient corresponding information of detected reflection points, e.g., distances, velocities, and angle information, can be exploited to synthesize motion velocity vectors to achieve a high signal-to-noise ratio (SNR) performance, which does not require knowledge of the relative position of the two radars. Furthermore, we utilize a long short-term memory (LSTM) network as well as the synthesized motion velocity vectors to classify different gestures, which can achieve a significantly high accuracy of gesture recognition with a 1600-sample data set, e.g., 98.0%. The experimental results also illustrate the robustness of the proposed gesture recognition systems, e.g., changing the environment background and adding new gesture performers.

A general skeleton-based action and gesture recognition framework for human–robot collaboration

Recognizing human actions is crucial for an effective and safe collaboration between humans and robots. For example, in a collaborative assembly task, human workers can use gestures to communicate with the robot, and the robot can use the recognized actions to anticipate the next steps in the assembly process, leading to improved safety and productivity. In this work, we propose a general framework for human action recognition based on 3D pose estimation and ensemble techniques, which allows to recognize both body actions and hand gestures. The framework relies on OpenPose and 2D to 3D lifting methods to estimate 3D joints for the human body and the hands, feeding then these joints into a set of graph convolutional networks based on the Shift-GCN architecture. The output scores of all networks are combined using an ensemble approach to predict the final human action. The proposed framework was evaluated on a custom dataset designed for human–robot collaboration tasks, named IAS-Lab Collaborative HAR dataset. The results showed that using an ensemble of action recognition models improves the accuracy and robustness of the overall system; moreover, the proposed framework can be easily specialized on different scenarios and achieve state-of-the-art results on the HRI30 dataset when coupled with an object detector or classifier.

A comprehensive framework for hand gesture recognition using hybrid-metaheuristic algorithms and deep learning models

A comprehensive framework for hand gesture recognition using hybrid-metaheuristic algorithms and deep learning models

Gaze-aware hand gesture recognition for intelligent construction

The advances in construction robotics in recent decades has been a powerful driver of construction automation. User-friendly interfaces to support human–robot work collaboration are critical for increasing adoption of construction robots. Among different interfaces, hand gesture is an effective and reliable interaction cue in the noisy construction environment. This paper proposes a novel human gaze-aware hand gesture recognition framework as a human–robot interface for intelligent construction. The proposed framework relies on an eye tracker to visually detect and track robotic machines in the first-person view. Then, the machine-of-interest is determined based on the bounding boxes of machines and gaze points. Finally, a hand gesture recognition architecture is incorporated with the machine information for conveying messages to the machine-of-interest. This approach was tested through a framework validation test and achieved precision and recall of 93.8% and 95.0%, respectively. A pilot study was conducted to demonstrate interaction with a robotic excavator and a dump truck, and the results illustrated that the proposed framework could serve as an effective interface for one-to-many human–robot collaborations in construction.

A Novel Machine Learning–Based Hand Gesture Recognition Using HCI on IoT Assisted Cloud Platform

Machine learning is a technique for analyzing data that aids the construction of mathematical models. Because of the growth of the Internet of Things (IoT) and wearable sensor devices, gesture interfaces are becoming a more natural and expedient human-machine interaction method. This type of artificial intelligence that requires minimal or no direct human intervention in decision-making is predicated on the ability of intelligent systems to self-train and detect patterns. The rise of touch-free applications and the number of deaf people have increased the significance of hand gesture recognition. Potential applications of hand gesture recognition research span from online gaming to surgical robotics. The location of the hands, the alignment of the fingers, and the hand-to-body posture are the fundamental components of hierarchical emotions in gestures. Linguistic gestures may be difficult to distinguish from nonsensical motions in the field of gesture recognition. Linguistic gestures may be difficult to distinguish from nonsensical motions in the field of gesture recognition. In this scenario, it may be difficult to overcome segmentation uncertainty caused by accidental hand motions or trembling. When a user performs the same dynamic gesture, the hand shapes and speeds of each user, as well as those often generated by the same user, vary. A machine-learning-based Gesture Recognition Framework (ML-GRF) for recognizing the beginning and end of a gesture sequence in a continuous stream of data is suggested to solve the problem of distinguishing between meaningful dynamic gestures and scattered generation. We have recommended using a similarity matching-based gesture classification approach to reduce the overall computing cost associated with identifying actions, and we have shown how an efficient feature extraction method can be used to reduce the thousands of single gesture information to four binary digit gesture codes. The findings from the simulation support the accuracy, precision, gesture recognition, sensitivity, and efficiency rates. The Machine Learning-based Gesture Recognition Framework (ML-GRF) had an accuracy rate of 98.97%, a precision rate of 97.65%, a gesture recognition rate of 98.04%, a sensitivity rate of 96.99%, and an efficiency rate of 95.12%.

SSEC: Semantic Segmentation and Ensemble Classification Framework for Static Hand Gesture Recognition using RGB-D Data

Hand Gesture Recognition (HGR) refers to identifying various hand postures used in Sign Language Recognition (SLR) and Human Computer Interaction (HCI) applications. Complex background in uncontrolled environmental condition is the major challenging issue which impacts the recognition accuracy of HGR system. This can be effectively addressed by discarding the background using suitable semantic segmentation method, where it predicts the hand region pixels into foreground and rest of the pixels into background. In this paper, we have analyzed and evaluated well known semantic segmentation architectures for hand region segmentation using both RGB and depth data. Further, ensemble of segmented RGB and depth stream is used for hand gesture classification through probability score fusion. Experimental results shows that the proposed novel framework of Semantic Segmentation and Ensemble Classification (SSEC) is suitable for static hand gesture recognition and achieved F1-score of 88.91% on OUHANDS test dataset.

An Electrode Configuration Framework for sEMG-Based Gesture Recognition With Muscle-Matched Layouts

An Electrode Configuration Framework for sEMG-Based Gesture Recognition With Muscle-Matched Layouts

Recognizing Teachers’ Hand Gestures for Effective Non-Verbal Interaction

Hand gesturing is one of the most useful non-verbal behaviors in the classroom, and can help students activate multi-sensory channels to complement teachers’ verbal behaviors and ultimately enhance teaching effectiveness. The existing mainstream detection algorithms that can be used to recognize hand gestures suffered from low recognition accuracy under complex backgrounds and different backlight conditions. This study proposes an improved hand gesture recognition framework based on key point statistical transformation features. The proposed framework can effectively reduce the sensitivity of images to background and light conditions. We extracted key points of the image and establish a weak classifier to enhance the anti-interference ability of the algorithm in the case of noise and partial occlusion. Then, we used a deep convolutional neural network model with multi-scale feature fusion to recognize teachers’ hand gestures. A series of experiments were conducted on different human gesture datasets to verify the performance of the proposed framework. The results show that the framework proposed in this study has better detection and recognition rates compared to the you only look once (YOLO) algorithm, YOLOv3, and other counterpart algorithms. The proposed framework not only achieved 98.43%, measured by F1 score, for human gesture images in low-light conditions, but also has good robustness in complex lighting environments. We used the proposed framework to recognize teacher gestures in a case classroom setting, and found that the proposed framework outperformed YOLO and YOLOv3 algorithms on small gesture images with respect to recognition performance and robustness.

Spatial and Temporal Hand-Raising Recognition from Classroom Videos Using Locality, Relative Position-Aware Non-Local Networks and Hand Tracking

Hand-raising gesture is one of the most popular signs of communication, whose frequency is related to the classroom’s atmosphere, the attractiveness of the subject, and the level of interactions between students and teachers. However, automatic hand-raising gesture detection and recognition remains still a challenging problem mainly due to low hand resolution, hand occlusion, various backgrounds, and viewpoint changes. While majority of the existing methods focus on static hand-raising posture detection, in this paper, we propose a framework for dynamic hand gesture recognition from classroom videos consisting of two main stages: hand posture detection and dynamic hand gesture recognition. In hand posture detection stage, we extend the previous work by adding relative position-aware in non-local network. After detecting the hand-raising posture on static pixels, which in turn accelerates the performance images, at the dynamic hand gesture recognition stage, we incorporate object detection and tracking to associate the hand-raising detection results at consecutive frames to supplement the missing detection due to the occlusion issue and obtain hand-raising gesture recognition at the event level. The experimental results show that the proposed method outperforms three benchmark models for static hand posture detection that are Faster-RCNN, Libra-RCNN, and Libra-RCNN+RDA on our dataset with the gains obtained for mAP being 7.68%, 5.76%, and 0.35% higher, respectively. In terms of event-level recognition results, the proposed method achieves the value of frame-wise accuracy, temporal_IoU, F1-score@0.3, and Levenshtein-score are 90.0%, 84.4%, 83.2%, and 84.3%, respectively. The code and dataset used in the paper will be made publicity available for research community.

Deep Learning Model for Dynamic Hand Gesture Recognition for Natural Human-Machine Interface on End Devices

As end devices have become ubiquitous in daily life, the use of natural human-machine interfaces has become an important topic. Many researchers have proposed the frameworks to improve the performance of dynamic hand gesture recognition. Some CNN models are widely used to increase the accuracy of dynamic hand gesture recognition. However, most CNN models are not suitable for end devices. This is because image frames are captured continuously and result in lower hand gesture recognition accuracy. In addition, the trained models need to be efficiently deployed on end devices. To solve the problems, the study proposes a dynamic hand gesture recognition framework on end devices. The authors provide a method (i.e., ModelOps) to deploy the trained model on end devices, by building an edge computing architecture using Kubernetes. The research provides developers with a real-time gesture recognition component. The experimental results show that the framework is suitable on end devices.

SEMG-Based Gesture Recognition Using Deep Learning From Noisy Labels.

Gesture recognition for myoelectric prosthesis control utilizing sparse multichannel surface Electromyography (sEMG) is a challenging task, and from a Muscle-Computer Interface (MCI) standpoint, the performance is still far from optimal. However, the design of a well-performed sEMG recognition system depends on the flexibility of the input-output function and the dataset's quality. To improve the performance of MCI, we proposed a novel gesture recognition framework that (i) Enrich the spectral information of the sparse sEMG signals by constructing a fused map image (denoted as sEMG-Map) that integrates a multiresolution decomposition (by means of orthogonal wavelets) through the raw signals then rely upon the Convolutional Neural Network (CNN) capacity to exploit the composite hierarchies in the constructed sEMG-Map input. (ii) Deals with the label noise by proposing a data-centric method (denoted as ALR-CNN) that synchronously refines the falsely labeled samples and optimizes the CNN model based on two basic assumptions. First, the deep model accuracy improves as the training progress. Second, a set of successive learnable max-activated outputs of a well-performed deep model is a reliable estimator for motion detection in the muscle activation pattern. Our proposed framework is evaluated on three large-scale public databases. The average classification accuracy is 95.50%, 95.85%, and 85.58% for NinaPro DB2, NinaPro DB7, and NinaPro DB3, respectively. The experimental results verify the effectuality of the proposed method and show high accuracy.

Deep Neural Network for Electromyography Signal Classification via Wearable Sensors

The human-computer interaction has been widely used in many fields, such intelligent prosthetic control, sports medicine, rehabilitation medicine, and clinical medicine. It has gradually become a research focus of social scientists. In the field of intelligent prosthesis, sEMG signal has become the most widely used control signal source because it is easy to obtain. The off-line sEMG control intelligent prosthesis needs to recognize the gestures to execute associated action. In order solve this issue, this paper adopts a CNN plus BiLSTM to automatically extract sEMG features and recognize the gestures. The CNN plus BiLSTM can overcome the drawbacks in the manual feature extraction methods. The experimental results show that the proposed gesture recognition framework can extract overall gesture features, which can improve the recognition rate.